Adaptive-quality image and video compression is a well-known art whereby variable amounts of bits are allocated to different spatial and/or temporal portions of the data to be compressed depending on the variable quality requirements of the particular application. Traditional approaches take as input uncompressed images or videos of a scene, determine the locations of regions of interest (based, for example, on visual saliency or on the requirements of the particular application, for example, teleconferencing) in the scene, and re-compress the image or video more efficiently (from a perceptual or application standpoint) by allocating larger amounts of bits to the regions of interest. The disadvantage of these traditional approaches is that they are wasteful since the original data is typically already compressed (for example, at acquisition), which requires performing decompression and adaptive re-compression. Compressive sensing technologies, on the other hand, are capable of performing image and/or video acquisition and compression simultaneously. Compressive sensing can be beneficial because it reduces the number of samples required to spatially and/or temporally reconstruct a given scene, thus enabling the use of inexpensive sensors with reduced spatial and/or temporal resolution in applications where complex sensors are otherwise used, while maintaining the quality of the reconstructed scene. To date, however, compressive sensing techniques with adaptive quality capabilities have not been proposed. What is desirable therefore are methods that can simultaneously offer the benefits provided by compressive sensing while at the same time enabling adaptive quality scene reconstruction. This is of particular interest in applications where the video camera is a multi-spectral or hyperspectral imaging system where the spatial sensor is expensive to manufacture.
Accordingly, what is needed in this art are increasingly sophisticated systems and methods which use a compressed sensing framework to process an image of a scene captured using a compressed sensing device such that when the image is reconstructed pixels associated with one or more regions of interest in that scene are rendered at a higher quality relative to other pixels in that image.